Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/14067
Title: Machine Learning Techniques with ECG and EEG Data: An Exploratory Study
Authors: Ponciano, Vasco
Pires, Ivan Miguel
Ribeiro, Fernando Reinaldo
Garcia, Nuno M.
Villasana, María Vanessa
Zdravevski, Eftim 
Lameski, Petre 
Issue Date: 29-Jun-2020
Publisher: MDPI AG
Journal: Computers
Abstract: <jats:p>Electrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results.</jats:p>
URI: http://hdl.handle.net/20.500.12188/14067
DOI: 10.3390/computers9030055
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles

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